{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "938cacb9",
   "metadata": {},
   "source": [
    "## 数据探索 Explore"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f05db70",
   "metadata": {},
   "source": [
    "(数据探索 / 探索性数据分析EDA Exploratory Data Analysis)\n",
    "\n",
    "通过搜索预期的关系、未预料的趋势和异常来探索数据，以获得理解和想法。\n",
    "EDA探索性数据分析和描述性统计包括统计总体数据量大小，好坏客户占比，数据类型有哪些，变量缺失率，变量频率分析直方图可视化，箱形图可视化，变量相关性可视化等。\n",
    "探索性数据分析方法很多常见的有：hist直方图、scater散点图,boxer箱线图,heat热力图，pairplot配对图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4071adcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from westat import *\n",
    "\n",
    "# 导入 UCI_Credit_Card\n",
    "data = uci_credit_card()\n",
    "\n",
    "# 将目标变量重命名为“y”\n",
    "data.rename(columns={'target':'y'},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82940e6a",
   "metadata": {},
   "source": [
    "### 查看数据分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "55822107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>Name</th>\n",
       "      <th>Value</th>\n",
       "      <th>#Count</th>\n",
       "      <th>%Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>y</td>\n",
       "      <td>0</td>\n",
       "      <td>23364</td>\n",
       "      <td>77.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>y</td>\n",
       "      <td>1</td>\n",
       "      <td>6636</td>\n",
       "      <td>22.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Total</td>\n",
       "      <td></td>\n",
       "      <td>30000</td>\n",
       "      <td>100.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No.   Name Value  #Count   %Ratio\n",
       "0    1      y     0   23364   77.88%\n",
       "1    2      y     1    6636   22.12%\n",
       "2    3  Total         30000  100.00%"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value_counts 函数默认用于查看目标变量的数据分布\n",
    "value_counts(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0f9a3d50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>Name</th>\n",
       "      <th>Value</th>\n",
       "      <th>#Count</th>\n",
       "      <th>%Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>2</td>\n",
       "      <td>14030</td>\n",
       "      <td>46.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>1</td>\n",
       "      <td>10585</td>\n",
       "      <td>35.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>3</td>\n",
       "      <td>4917</td>\n",
       "      <td>16.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>5</td>\n",
       "      <td>280</td>\n",
       "      <td>0.93%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>4</td>\n",
       "      <td>123</td>\n",
       "      <td>0.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>6</td>\n",
       "      <td>51</td>\n",
       "      <td>0.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Total</td>\n",
       "      <td></td>\n",
       "      <td>30000</td>\n",
       "      <td>100.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No.       Name Value  #Count   %Ratio\n",
       "0    1  EDUCATION     2   14030   46.77%\n",
       "1    2  EDUCATION     1   10585   35.28%\n",
       "2    3  EDUCATION     3    4917   16.39%\n",
       "3    4  EDUCATION     5     280    0.93%\n",
       "4    5  EDUCATION     4     123    0.41%\n",
       "5    6  EDUCATION     6      51    0.17%\n",
       "6    7  EDUCATION     0      14    0.05%\n",
       "7    8      Total         30000  100.00%"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value_counts 函数指定需要查看的变量名称，用于该变量的数据分布\n",
    "value_counts(data,col='EDUCATION')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6af583b",
   "metadata": {},
   "source": [
    "### 通过图形查看数据分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f0f28573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot_counts 函数默认用于查看目标变量的数据分布\n",
    "plot_counts(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "150cc3c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot_counts 函数指定需要查看的变量名称，用于该变量的数据分布\n",
    "plot_counts(data,col='EDUCATION')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c80b4b31",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>序号</th>\n",
       "      <th>名称</th>\n",
       "      <th>值</th>\n",
       "      <th>#数量</th>\n",
       "      <th>%占比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>2</td>\n",
       "      <td>14030.00</td>\n",
       "      <td>46.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>1</td>\n",
       "      <td>10585.00</td>\n",
       "      <td>35.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>3</td>\n",
       "      <td>4917.00</td>\n",
       "      <td>16.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>5</td>\n",
       "      <td>280.00</td>\n",
       "      <td>0.93%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>4</td>\n",
       "      <td>123.00</td>\n",
       "      <td>0.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>6</td>\n",
       "      <td>51.00</td>\n",
       "      <td>0.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Total</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>30000.00</td>\n",
       "      <td>100.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      序号         名称  值       #数量      %占比\n",
       "0      1  EDUCATION  2  14030.00   46.77%\n",
       "1      2  EDUCATION  1  10585.00   35.28%\n",
       "2      3  EDUCATION  3   4917.00   16.39%\n",
       "3      4  EDUCATION  5    280.00    0.93%\n",
       "4      5  EDUCATION  4    123.00    0.41%\n",
       "5      6  EDUCATION  6     51.00    0.17%\n",
       "6      7  EDUCATION  0     14.00    0.05%\n",
       "7  Total                30000.00  100.00%"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_counts(data: pd.DataFrame,\n",
    "             col: str = 'y',\n",
    "             missing: list = [np.nan, None, 'nan','null','NULL'],\n",
    "             color: list = ['#1f77b4', '#ff800b', '#d62728', '#333333'],\n",
    "             marker: str = 'o',\n",
    "             linewidth: int = 2,\n",
    "             linestyle: str = '-',\n",
    "             style='default',\n",
    "             figsize: tuple = (8, 5),\n",
    "             return_data: bool = False,\n",
    "             precision: int = 2,\n",
    "             language='en'):\n",
    "    \"\"\"\n",
    "    绘图展示指定列在数据集中的分布情况，例如每个取值的数量、占比\n",
    "    Args:\n",
    "        data: DataFrame,需要检查数据分布的数据集\n",
    "        col: str,需要检查的列名\n",
    "        missing: list,缺失值列表\n",
    "        color: 条形图颜色名称,默认为['#1f77b4', '#ff800b', '#d62728', '#333333'],\n",
    "        marker: 折线图标记样式，默认为o，即圆点\n",
    "        linewidth: 折线图的线宽，默认为1\n",
    "        linestyle: 折线图中线的类型，默认为 '-'\n",
    "        style: 绘图的样式风格\n",
    "        figsize: 图片大小，默认为(8,5)\n",
    "        return_data: bool,是否返回计算结果数据\n",
    "        precision: int,数据精度，小数点位数，默认为2\n",
    "        language: str,数据结果标题列显示语言，默认为 'en',可手动修改为'cn'\n",
    "\n",
    "    Returns:\n",
    "        绘图展示指定列在数据集中的分布情况\n",
    "    \"\"\"\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.rcParams['font.sans-serif'] = 'SimHei'  # 设置中文字体\n",
    "    plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "    result = get_data_distribution(data,col=col,precision=precision,language=language)\n",
    "    plot_result =result.iloc[:-1, :]\n",
    "\n",
    "    x = pd.to_numeric(result.iloc[:,2])\n",
    "    y = pd.to_numeric(result.iloc[:,3])\n",
    "    z = pd.to_numeric(result.iloc[:,4].apply(lambda x: (x[:-1]))) / 100\n",
    "\n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    ax1 = fig.add_subplot(1, 1, 1)\n",
    "    ax1.bar(x, y, bottom=0, align='center', color=color, label='Count')\n",
    "\n",
    "    # 设置计数标签\n",
    "    for a, b, c in zip(x, y, y):\n",
    "        ax1.text(a, b / 2, b, ha='center', va='center', fontsize=12, color=color[3])\n",
    "\n",
    "    ax1.set_title(col)\n",
    "    ax1.set_xticks(x, result.index)\n",
    "    ax1.set_xlabel(col)\n",
    "    ax1.set_ylabel('Count')\n",
    "\n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.plot(x, z, marker=marker,\n",
    "             color=color[2],\n",
    "             linewidth=linewidth,\n",
    "             linestyle=linestyle,\n",
    "             label='Ratio')\n",
    "    ax2.set_ylabel('Ratio')\n",
    "\n",
    "    # 设置比例标签\n",
    "    for a, b in zip(x, z):\n",
    "        if b < 0:\n",
    "            p = b - z.max() / 100\n",
    "        elif b == z.max():\n",
    "            p = b - z.max() / 100\n",
    "        else:\n",
    "            p = b + z.max() / 100\n",
    "\n",
    "        ax2.text(a + 0.1, p, format(b, '.2%'), ha='center', va='bottom', fontsize=12, color=color[2])\n",
    "\n",
    "    fig.legend(loc=1)\n",
    "    plt.show()\n",
    "\n",
    "    # 返回数据结果\n",
    "    if return_data:\n",
    "        return result\n",
    "    \n",
    "plot_counts(data,col='EDUCATION',return_data=True,language='cn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fc2c4405",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data_distribution(data: pd.DataFrame,\n",
    "                          col='y',\n",
    "                          by=['#Count', 'Name'],\n",
    "                          ascending=[False, False],\n",
    "                          precision=2,\n",
    "                          language='en') -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    查看数据集data中col列的数据分布情况\n",
    "    Args:\n",
    "        data:DataFrame,需要检查数据分布的数据集\n",
    "        by:list,根据指定列进行排序，默认根据数量（降序）、名称（升序）排序\n",
    "        ascending:list,指定列对应的排序列表\n",
    "        col:需要检查数据分布的列，默认为'y'\n",
    "        display:bool,是否显示详细信息\n",
    "        precision:数据精度，默认为2位小数\n",
    "        language:显示语言，en：显示为英文，cn：显示为中文\n",
    "    Returns:\n",
    "        返回包含数据量、数据占比的数据集\n",
    "    \"\"\"\n",
    "    result = pd.DataFrame(data=data[col].value_counts())\n",
    "    result.columns = ['count']\n",
    "    result['%Ratio'] = result['count'] / len(data)\n",
    "    result['Value'] = result.index\n",
    "    result['Name'] = col\n",
    "    result = result.rename(columns={'count': '#Count'})\n",
    "    result = result[['Name', 'Value', '#Count', '%Ratio']]\n",
    "\n",
    "    # 排序\n",
    "    result.sort_values(by=by, ascending=ascending, inplace=True)\n",
    "    \n",
    "    # 添加汇总行\n",
    "    total = result.iloc[:, 1:].apply(lambda x: x.sum())\n",
    "    row = pd.DataFrame([''] + total.to_list()).T\n",
    "    row.columns = result.columns\n",
    "    result = pd.concat([result, row], ignore_index=True)\n",
    "\n",
    "    result.reset_index(drop=True, inplace=True)\n",
    "    result['No.'] = result.index + 1\n",
    "    \n",
    "\n",
    "    # 数据精度设置\n",
    "    result['#Count'] = result['#Count'].apply(lambda x: format(x, '.' + str(precision) + 'f'))\n",
    "    result['%Ratio'] = result['%Ratio'].apply(lambda x: format(x, '.' + str(precision) + '%'))\n",
    "    \n",
    "    result = result[['No.', 'Name', 'Value', '#Count', '%Ratio']]\n",
    "    result.iat[-1,0] = 'Total'\n",
    "    result.iat[-1,1] = ''\n",
    "    result.iat[-1,2] = ''\n",
    "    \n",
    "\n",
    "    # 标题栏语言设置\n",
    "    if language == 'cn':\n",
    "        result.rename(columns={'No.': '序号', 'Name': '名称', 'Value': '值', '#Count': '#数量', '%Ratio': '%占比'},\n",
    "                        inplace=True)\n",
    "\n",
    "\n",
    "    return result\n",
    "\n",
    "df=get_data_distribution(data,precision=4,language='cn')"
   ]
  }
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